Deep evolutionary analysis reveals the design principles of fold A glycosyltransferases

Glycosyltransferases (GTs) are prevalent across the tree of life and
regulate nearly all aspects of cellular functions. The evolutionary basis
for their complex and diverse modes of catalytic functions remain
enigmatic. Here, based on deep mining of over half million GT-A fold
sequences, we define a minimal core component shared among functionally
diverse enzymes. We find that variations in the common core and emergence
of hypervariable loops extending from the core contributed to GT-A
diversity. We provide a phylogenetic framework relating diverse GT-A fold
families for the first time and show that inverting and retaining
mechanisms emerged multiple times independently during evolution. Using
evolutionary information encoded in primary sequences, we trained a
machine learning classifier to predict donor specificity with nearly 90%
accuracy and deployed it for the annotation of understudied GTs. Our
studies provide an evolutionary framework for investigating complex
relationships connecting GT-A fold sequence, structure, function and
regulation.